Acta Photonica Sinica, Volume. 49, Issue 7, 710003(2020)

Infrared Small Target Detection Based on Fully Convolutional Neural Network and Visual Saliency

Jun-ming LIU1 and Wei-hua MENG1,2
Author Affiliations
  • 1China Airborne Missile Academy, Luoyang, Henan 471009, China
  • 2Aviation Key Laboratory of Science and Technology on Airborne Guided Weapons, Luoyang, Henan 471009, China
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    Figures & Tables(15)
    Proposed detection scheme
    Detection schemes of different algorithms
    Typical fully convolutional neural networks for infrared small target detection.
    Proposed fully convolutional neural network
    Typical target image based on 2D Gaussian model
    Typical infrared images with small targets
    Segmentation results of different networks
    ROC curve of proposed algorithms with and without contrast feature
    Test images and results of seven algorithms
    ROC curves of different algorithms
    • Table 1. Comparation of different design choices of proposed network

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      Table 1. Comparation of different design choices of proposed network

      TypeDescriptionPdFaGFLOPs
      M1Base network in Fig. 40.940 87.97×10-60.235
      M11Without down-sampling layer0.938 92.01×10-50.494
      M12Without feature of 3rd conv layer0.929 41.11×10-50.226
      M13Without SE layer0.938 91.42×10-50.235
    • Table 2. Performance of different networks

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      Table 2. Performance of different networks

      TypeDescriptionPdFaGFLOPs
      M1Network in Fig. 40.940 87.97×10-60.235
      M2Network in Fig. 3(a)0.931 31.06×10-56.47
      M3Network in Fig. 3(b)0.933 24.23×10-51.54
    • Table 3. G of different algorithms on 8 test images

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      Table 3. G of different algorithms on 8 test images

      IndexADMDIPIMLIGMaxMedianFMDNNFCN+CNNS-FCN
      120.378.931.012.025.163.6118.0
      217.900.14.815.221.028.9
      310.138.96.412.75.317.155.9
      416.917.920.515.919.025.925.3
      58.212.59.61.85.96.912.6
      656.10.01.950.333.5310.8520.7
      75.224.23.819.57.29.929.8
      812.537.035.214.54.419.751.3
    • Table 4. B of different algorithms on 8 test images

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      Table 4. B of different algorithms on 8 test images

      IndexADMDIPIMLIGMaxMedianFMDNNFCN+CNNS-FCN
      14.219.96.32.55.112.823.7
      25.3--1.44.56.28.5
      34.119.65.05.12.16.822.3
      46.18.27.45.86.99.49.2
      52.23.42.60.91.61.93.4
      63.7--3.01.917.929.3
      72.313.63.18.33.14.312.7
      86.627.718.47.62.410.326.8
    • Table 5. Average run time of different algorithms

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      Table 5. Average run time of different algorithms

      MethodADMDIPIMLIGMaxMedianFMDNNFCN+CNNS-FCN
      Time/s0.9960.8660.2840.2190.0660.1640.033
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    Jun-ming LIU, Wei-hua MENG. Infrared Small Target Detection Based on Fully Convolutional Neural Network and Visual Saliency[J]. Acta Photonica Sinica, 2020, 49(7): 710003

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    Paper Information

    Category: Image Processing

    Received: Mar. 1, 2020

    Accepted: --

    Published Online: Aug. 25, 2020

    The Author Email:

    DOI:10.3788/gzxb20204907.0710003

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